Learning to detect stereo saliency

This paper develops a novel learning-based method for detecting stereo saliency in stereopair images. The disparity maps computed from stereopair images provide an additional depth cue for stereo saliency detection. To the best of our knowledge, our approach is the first one to simultaneously detect the stereo saliency of both left and right images using support vector machine (SVM). In our work, the disparity maps are used in two aspects. One is to improve the performance of saliency detection for monocular image. The other one is to maintain the consistency between the stereo matching and saliency maps. In order to meet the above requirements, we propose a new combinational saliency feature to train the stereo images with the labeled saliency ground truth, using support vector machine as the classifier. In the test stage, our approach generates the stereo saliency results according to the trained SVM model. Furthermore, a stereopair saliency dataset containing 400 pairs of images is created to perform the challenging experiments. The experimental results have demonstrated that our method achieves better performance than the state-of-the-art algorithms of single-image saliency detection.